| ABR |
Antibiotic Resistance (Stebliankin et al, 2022) |
| ADHD |
ADHD 16S gut microbiome analysis (Cickovski et al, 2023) |
| Alpha1 |
Alpha-1 Antitrypsin Deficiency multiomics analysis (Cickovski et al, 2019) |
| AmpliconNoise |
Remove noise from 454 amplicons (Quince et al, 2011) |
| AmpliQue |
Bacterial identification using 16S primer data (Gonzalez et al, 2008) |
| AMR |
Antimicrobial resistance (Steblinakin et al, 2022) |
| Antibiotics |
Influence of antibiotics on bacterial growth (Stebliankin et al, 2021) |
| ASV |
Convert raw metagenomics data to Amplicon Sequence Variants (ASVs) (Callahan et al, 2017) |
| BacterialGrowth |
Bacterial growth using peak-to-trough ratio (Stebliankin et al, 2020) |
| Bayes |
Bayesian network (Auguet et al, 2021) |
| Benchmark |
Challenges in benchmarking metagenomic profilers (Sun et al, 2021) |
| BRENDA |
Multiomics analysis using the BRENDA (Casero-Cheema et al, 2017) enzyme database |
| Cache |
Machine learning and cache block replacement (Yusuf et al, 2021) |
| Cancer |
Pangenome cancer genome analysis (Poore et al, 2020) |
| CaReT |
Classification And REgression Training (Kuhn et al, 2019) |
| Causality |
Compute causal networks from microbiome abundance data (Sazal et al, 2019) |
| COVID19 |
Molecular mimicry with Covid-19 Spike protein (Nunez-Castilla et al, 2022) |
| CysticFibrosis |
16SrRNA variable regions, cystic fibrosis study (Doud et al, 2010) |
| DBN |
Build a Dynamic Bayesian Network from longitudinal microbiome data (Ruiz-Perez et al, 2019) |
| Electricity |
Apply transformers to predict electricity consumption (Li et al 2020, Sama 2020) |
| Environment |
Rubin causal model for estimating environmental exposure effects on microbiome (Sommer et al, 2021) |
| Epitopedia
| Identifying molecular mimicry between pathogens and known immune epitopes (Balbin et al, 2022) |
| Flint |
Large-scale microbiome analysis in the cloud (Valdes et al, 2019) |
| Flood |
Flood prediction using machine learning (Shi et al, 2022) |
| Glutamine |
Analysis of glutamine metabolite (Cickovski et al, 2021) |
| GPU |
Miscellaneous GPU-based pipelines using NVIDIA Kits (NVIDIA, 2016) |
| GulfWar |
Mouse Gulf War Illness study (Seth et al, 2022) |
| HELIUS |
Analysis of the HELIUS dataset (Nayman et al, 2023) |
| HIVEvoNets |
Evolutionary networks of sequence data and application to HIV (Buendia and Narasimhan, 2006) |
| Immunotherapy |
Impact of immunotherapy on cancer microbiome (Stebliankin et al, 2019) |
| InfantGut |
Infant gut microbiome analysis (Ruiz-Perez et al, 2019) |
| Jasper |
Microbiome map assembly (Valdes et al, 2021) |
| Kidney |
Kidney microbiome analysis (Park et al, 2020) |
| MASH |
Fast genome and metagenome distance estimation (Ondov et al, 2016) |
| MathTest |
Starter pipeline for PluMA, that performs basic arithmetic (Vargas, 2022) |
| MeRRCI |
Metagenome, Resistome, Replicome for Causal Inferencing (Stebliankin et al, 2022) |
| Metabolomics |
Metabolomics pipeline (Stebliankin et al, 2022) |
| Metronidazole |
Effect of metronidazole on vaginal microbiota (Ruiz-Perez et al, 2021) |
| MOSAIC |
MOSAIC pipeline to compute Index of Replication (Stebliankin et al, 2020) |
| Mouse |
Metagenomics pipeline to analyze 16S mouse gut microbiome data, from the PluMA userguide (Kozich et al, 2013) |
| MSN |
Produce microbial social networks (Fernandez et al, 2015) |
| Opioid |
Microbiome-related relationships between cocaine use and metabolites (Martinez et al, 2022) |
| OriginOfReplication |
Compute the origin of replication (Brown et al, 2016) |
| OTU_ASV |
OTU-ASV comparison on 16S cohort gut microbiome dataset (Nayman et al, 2022) |
| PALM |
Pipeline for the Analysis of Longitudinal Multi-omics time-series data (Ruiz-Perez et al, 2021) |
| Parkinsons |
16S gut microbiome dysbiosis in Parkinson's Disease patients (Pietrucci et al, 2019) |
| PetRI |
PetRi pipeline for determining bacterial replication rates (Stebliankin et al, 2020) |
| PIsToN |
Protein binding Interfaces with Transformer Networks (Stebliankin et al, 2023) |
| Plots |
Split a dataset, run compositional and differential analyses, compute correlations, produce plots (Aguilar-Pulido et al, 2015) |
| PLSDASoYouThink |
Partial Last Square Discriminant Analysis (PLSDA) Performance (Ruiz-Perez et al, 2020) |
| PM16S |
Downstream 16S mouse gut microbiome analysis, pre- and post-obesity (Cickovski and Narasimhan, 2018) |
| PRIMAL |
Pipeline for Retrieval of Interactions in the Microbiome from temporAL data (Lugo-Martinez et al, 2019) |
| ProcessAbund |
Take raw abundances, normalize and threshold them, and split into groups (Riveros et al, 2014) |
| Qiime2 |
Qiime2 metagenomics pipeline (Polanco et al, 2020) |
| RAPToR |
Resistome Abundance and Peak-to-Trough Ratio Pipeline (Stebliankin et al, 2022) |
| RedTide |
Gut microbiome analysis of red-tide microcystin exposed mice (Saha et al, 2022) |
| SAHMI |
Denoising sparse microbial signals from single-cell sequencing of mammalian host tissues (Ghaddar et al, 2022) |
| Smoking |
Smoker 16S lung microbiome study (Campos et al, 2022) |
| Tara |
Structure and function of the global ocean microbiome (Sunagawa et al, 2015) |
| T2D |
Type-2 Diabetes Mellitus (Nayman et al, 2024) |
| Tensorflow |
Pipeline with miscellaneous Tensorflow utilities (Rodriguez and Riggs, 2016) |
| Tuxedo |
Differential gene and transcript expression analysis (Trapnell et al, 2012) |
| Viral |
Viral challenge (2016) |
| VPhyloMM |
Viral mutational pathways of drug resistance (Buendia et al, 2009) |